Exceedance probability of some arsenic threshold values

The alluvial aquifers of the Po and Venetian Plain show high concentration of arsenic principally in reducing environments and it is often associated with the presence of organic matter. Our study concerns the Drainage Basin to the Venice Lagoon (DBVL) that is a densely populated area extending on the provinces of Venice, Padua and Treviso for approximately 2038 km2. Notoriously the arsenic contamination of groundwater affects the DBVL. The UE’s Groundwater Directive (GWD 2006/118/EC) suggests an arsenic Contamination Threshold Values (CTV) equal to 10 μg/l. In addition, the UE BRIDGE project proposes to use the 90th percentile of the concentration data to estimate the Natural Background Level (NBL) (Müller et al., 2006). Nevertheless, this method provides only a statistical NBL value for the whole area without considering the spatial variation of the As concentration. Thus, the aim of this study is to improve the NBL concept using a geostatistical approach on a sensitive case where the NBL is seven times higher than the CTV. The dataset used in this work comes from the “A.Li.Na” project (founded by the Regional Environmental Agency) that aimed to define the NBLs of As, Fe, Mn and NH4 + into the DBVL’s groundwater. Four seasonal surveys, from 2013 to 2014, collected the hydro-geochemical parameters in 50 piezometers. Primarily, we defined two thresholds that correspond respectively to the CTV and the median of the data over the CTV. These values were decided on the base of the dataset’s statistical structure and the GWD 2006/118/EC. Subsequently, we evaluated the spatial distribution of the probability to exceed the defined thresholds using a geostatistical approach based on Indicator kriging. The results highlight different zones with high exceeding probability respect both the CTV and the median value. In particular, some portions of the study area have an exceeding probability ranging from 75% to 95% for both thresholds. Considering the geological setting of the DBVL, these high exceeding probability values correspond with the occurrence of both organic matter and reducing conditions. In conclusion, the spatial prediction of the exceeding probability could be useful to define the regions in which estimate the local NBLs, improving the procedure of NBL definition. In this way, the estimation of the NBL could be more realistic because it considers the spatial distribution of the studied chemical element/compound, distinguishing areas with a high natural concentration from polluted areas.